arXiv cs.AI· Yu He, Da Huang, Zhenyang Liu, Zixiao Gu, Qiang Sun, Guangnan Ye, Yanwei Fu, Yu-Gang Jiang·· 12 小时前AI 评分34
Schrödinger's Navigator：用想象未来集合实现零样本物体导航
Schr\"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation
AI 导读
Schrödinger's Navigator 提出一种信念感知框架，在推理时对多条轨迹条件下的想象 3D 未来进行推理，而非锁定单一地图。其轨迹条件 3D 世界模型预测假设观测并维持合理场景实现的多重叠加，配合自适应遮挡感知采样器和 Future-Aware Value Map（FAVM）聚合想象未来以选择动作。
正文 · 原文
Abstract:Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots. Yet methods that perform well in simulation often degrade in cluttered real-world scenes with severe occlusion and latent hazards, where large unseen regions make single-scene inference brittle and unsafe. We propose Schrödinger's Navigator, a belief-aware framework that reasons at inference time over multiple trajectory-conditioned imagined 3D futures. Given candidate paths, a trajectory-conditioned 3D world model predicts hypothetical observations and maintains a superposition of plausible scene realizations rather than committing to one map. An adaptive occluder-aware sampler directs imagination to uncertainty-critical regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures for robust, proactive action selection. Experiments in simulation and on a physical Go2 quadruped show that Schrödinger's Navigator outperforms strong ZSON baselines, improving hidden-target discovery and risk-aware waypoint selection in occlusion-heavy navigation scenarios. These results highlight imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.
| Comments: | Accepted to the Conference on Robot Learning (CoRL) 2026 |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2512.21201 [cs.RO] |
| (or arXiv:2512.21201v5 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2512.21201 arXiv-issued DOI via DataCite |
Submission history
From: Yu He [view email]
[v1]
Wed, 24 Dec 2025 14:28:17 UTC (7,651 KB)
[v2]
Tue, 24 Mar 2026 11:27:59 UTC (3,085 KB)
[v3]
Fri, 12 Jun 2026 10:14:19 UTC (7,496 KB)
[v4]
Wed, 14 Oct 2026 09:02:52 UTC (8,718 KB)
[v5]
Fri, 16 Oct 2026 07:12:44 UTC (8,718 KB)
来源：arXiv cs.AI · arxiv.org